Methods and apparatus to determine locations of audience members

ABSTRACT

A disclosed example method involves determining, using a neural network at a stationary unit, a first distance of a first portable audio detector from the stationary unit, the first portable audio detector associated with a first panelist, the stationary unit located in proximity to the media presentation device. The example method also involves determining, using the neural network, a second distance of a second portable audio detector from the stationary unit, the second portable audio detector associated with a second panelist. In response to the first distance being less than a threshold distance, the media is credited as exposed to the first panelist. In response to the second distance being more than the threshold distance, the media is not credited as exposed to the second panelist.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 12/967,415, filed on Dec. 14, 2010, which is hereby incorporated byreference herein in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to monitoring media and, moreparticularly, to methods and apparatus to determine locations ofaudience members.

BACKGROUND

Consuming media presentations generally involves listening to audioinformation and/or viewing video information such as, for example, radioprograms, music, television programs, movies, still images, etc.Media-centric companies such as, for example, advertising companies,broadcasting networks, etc. are often interested in the viewing andlistening interests of their audience to better market their products. Atechnique often used to measure the number and/or demographics ofaudience members exposed to media involves awarding media exposurecredit to a media presentation each time an audience member is exposedto (e.g., is in the vicinity of) the media presentation.

The media exposure activities of audience members may be monitored usingpersonal portable metering devices (PPMs), which are also known asportable meters, portable metering devices, and/or portable personalmeters. A PPM is an electronic device that is typically worn (e.g.,clipped to a belt or other apparel) or carried by an audience member.Some PPMs are configured to monitor media exposure by detecting orcollecting information (e.g., program or source identifier codes,signatures, etc.) from audio and/or video signals that are emitted orpresented by media delivery devices (e.g., televisions, stereos,speakers, computers, etc.).

An audience member or monitored individual performs their usual dailyroutine while wearing a PPM. The monitored individual's daily routinemay include listening to the radio and/or other sources of audio mediaand/or watching television programs and/or other sources of audio/visualmedia. As the audience member is exposed to (e.g., views, listens to, isin the vicinity of, etc.) media, a PPM associated with (e.g., assignedto and carried by) that audience member generates monitoring data.

Unfortunately, the typical household presents unique monitoringchallenges to the PPM. For example, a typical household includesmultiple media delivery devices, each configured to deliver mediacontent to specific viewing and/or listening areas located within thehome. A PPM, carried by a person who is located in one of the viewingand/or listening areas, is configured to detect any media content beingdelivered in the viewing and/or listening area and to credit theprogramming associated with the media content as having been deliveredto the corresponding audience member. Thus, the PPM operates on thepremise that any media content detected by the PPM is associated withprogramming to which the person carrying the PPM was exposed. However,in some cases, a PPM may detect media content that is emitted by a mediadelivery device that is not located within the viewing or listeningproximity of the person carrying the PPM, thereby causing the detectedprogramming to be improperly credited.

The ability of the PPM to detect audio/video content being deliveredoutside of the viewing and/or listening proximity of the person carryingthe PPM is an effect referred to as “spillover” because the mediacontent being delivered outside of the viewing and/or listeningproximity of the person carrying the PPM is described as “spilling over”into the area occupied by the person carrying the PPM. Spillover mayoccur, for example, in a case where a monitored individual in a bedroomis reading a book, but their PPM detects audio/video content deliveredby a television in an adjacent living room (i.e., outside of theirviewing/listening proximity), thereby causing the person carrying thePPM to be improperly credited as a member of the audience for theaudio/video content.

Another effect, referred to as “hijacking” occurs when a person's PPMdetects audio/video content being emitted from multiple media deliverydevices at the same time. For example, an adult watching a televisionnews program in a household kitchen may be located near a householdfamily room in which children are watching a television cartoon programon a different television. Yet, the cartoon programming delivered by thefamily room television may, in some cases, have signals that overpoweror “hijack” the signals associated with the news programming beingemitted by the kitchen television. As a result, information collected bythe adult's PPM may lead to inaccurately crediting the cartoon programas having been viewed by the adult and failing to credit the newsprogram with any viewing. Still further, other common difficulties suchas varying volume levels, varying audio/video content type (e.g.,sparse, medium, rich, etc.), varying household transmissioncharacteristics due to open/closed doors, movement and/or placement offurniture, acoustic characteristics of room layouts, wall construction,floor coverings, ceiling heights, etc. may lead to inaccurateaudio/video content exposure detection by PPMs.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example monitored environment in which locations ofaudience members may be detected.

FIG. 2 depicts an example apparatus constructed in accordance with theteachings of this disclosure and that may be used to detect locations ofaudience members in the monitored environment of FIG. 1 based ontime-domain analyses.

FIG. 3 depicts another example apparatus constructed in accordance withthe teachings of this disclosure and that may be used to detectlocations of audience members in the monitored environment of FIG. 1based on frequency-domain analyses.

FIGS. 4A-4C depict reference and non-stationary audio sample sets atdifferent time-shift positions relative to one another.

FIG. 5 depicts an example correlator of the example apparatus of FIG. 3determining correlation coefficients based on frequency-domainrepresentations of the reference and non-stationary audio sample sets ofFIG. 3.

FIGS. 6A and 6B depict a flow diagram representative of example machinereadable instructions that may be executed to determine locations ofaudience members.

FIG. 7 depicts a flow diagram representative of example machine readableinstructions that may be executed to determine correlation coefficientsfor the process of FIG. 6A based on time-domain analyses when a DC biasis not present in any audio sample set being analyzed.

FIGS. 8A and 8B depict a flow diagram representative of example machinereadable instructions that may be executed to determine correlationcoefficients for the process of FIG. 6A based on time-domain analyseswhen a DC bias is present in an audio sample set being analyzed.

FIG. 9 depicts a flow diagram representative of example machine readableinstructions that may be executed to determine correlation coefficientsfor the process of FIG. 6B based on frequency-domain analyses when a DCbias is not present in any audio sample set being analyzed.

FIGS. 10A-10C depict a flow diagram representative of example machinereadable instructions that may be executed to determine correlationcoefficients for the process of FIG. 6B based on frequency-domainanalyses when a DC bias is present in an audio sample set beinganalyzed.

FIGS. 11A-11C depict a flow diagram representative of example machinereadable instructions that may be executed to determine correlationcoefficients for the process of FIG. 6B based on frequency-domainanalyses when a DC bias is present in both audio sample sets beinganalyzed or when it is unknown whether a DC bias is present in one orboth audio sample sets being analyzed.

FIG. 12 is an example processor system that can be used to execute theexample instructions of FIGS. 6A, 6B, 7, 8A, 8B, 9, 10A-10C, and 11A-11Cto implement the example apparatus and/or systems of FIGS. 1, 2, and 3described herein.

DETAILED DESCRIPTION

Example methods, apparatus, systems, and articles of manufacturedisclosed herein determine locations of audience members in monitoredenvironments. Such audience member location information may beadvantageously used to determine whether audience members were locatedsufficiently proximate to media devices (e.g., televisions, radios,computers, etc.) to have been exposed to media content presented bythose media devices.

Example methods, apparatus, systems, and articles of manufacturedisclosed herein involve using neural network models to determinelocations of audience members. To implement location detectiontechniques, an example location detection system is installed at a homeor other monitored environment (e.g., an office, a commercialestablishment, a restaurant, a bar, a retail store, etc.) and calibratedfor that environment. The location detection system includes a referenceunit that operates as a stationary audio detector and a portable unitthat operates as a portable audio detector. The reference unit includesa microphone and is positioned at a fixed location in the monitoredenvironment. The portable unit also includes a microphone and is worn orcarried by an audience member as the audience member moves in themonitored environment. During a calibration process, a neural networkmodel of the location detection system is trained based on knowndistances between the reference unit and one or more audio emitters(e.g., speakers) of one or more media devices in the monitoredenvironment and known distances between the portable unit and the audioemitter(s). The known distances and amplitude characteristics of audiodetected at the reference and portable units are input to the neuralnetwork model.

After calibration of the location detection system, and duringoperation, an audience member carries or wears the portable unit. Theportable unit digitizes any detected audio and communicates thedigitized audio samples to the location detection system, while thereference unit also detects and digitizes the same audio. The locationdetection system then correlates the portable unit audio samples (i.e.,non-stationary audio samples) with the reference unit audio samples(i.e., reference audio samples) to find one or more correlationcoefficients (e.g., maximum and/or minimum correlation coefficients).The correlation coefficients are then provided to the neural networkmodel, which determines a distance from the portable unit to the sourceof the detected audio (e.g., a speaker of a media device that emittedthe audio) based on its previous training.

The resulting distance measure provided by the neural network model canthen be used to determine whether the audience member carrying orwearing the portable unit was sufficiently close to the media devicethat emitted the audio to credit corresponding media content (e.g., atelevision program, a radio program, an advertisement, Internet webcontent, computer content, or any other media content) with exposure tothe audience member.

In some examples, the location detection system is configured to processacquired audio samples from the reference unit and acquired audiosamples from the portable unit using time-domain analysis techniques todetermine correlation coefficients. In other examples the locationdetection system is configured to use frequency-domain analysis toprocess the acquired audio samples to determine the correlationcoefficients.

FIG. 1 depicts an example monitored environment 100 having an examplelocation detection system 102 constructed in accordance with theteachings of this disclosure located therein. In the illustratedexample, the location detection system 102 includes a processing unit104 and a stationary reference unit 106. In the illustrated example, thelocation detection system 102 also includes or is in communication witha portable meter 108 carried or worn by a person 110 as the person 110moves in the monitored environment 100. In the illustrated example, thelocation detection system 102 is configured to determine the location(s)of the person 110 relative to a media presentation device 112 in themonitored environment 100. In the illustrated example, the mediapresentation device 112 is a television, but it may alternatively be astereo, a computer, a multi-media device, or any other mediapresentation device capable of presenting media content sought to bemonitored for exposure to the person 110.

In the illustrated example, the stationary reference unit 106 is locatednext to or substantially close to the media presentation device 112 suchthat distances between the portable meter 108 and the media presentationdevice 112 are the same or substantially similar to distances betweenthe portable meter 108 and the stationary reference unit 106. In thismanner, distances determined by the location detection system 102between the portable meter 108 and the stationary reference unit 106 areregarded as the same distances between the portable meter 108 and themedia presentation device 112.

In the illustrated example, the stationary reference unit 106 operatesas a stationary audio detector to detect audio emitted from the mediapresentation device 112 of the monitored environment 100. In theillustrated example, the stationary reference unit 106 digitizes thedetected audio and stores time-stamped digitized audio samples of thedetected audio. In other examples, the stationary reference unit 106sends the detected audio in analog form to the processing unit 104 andthe processing unit 104 digitizes and stores time-stamped digitizedaudio samples of the detected audio. In some examples, the stationaryreference unit 106 is provided with a microphone to detect the audioemitted by the media presentation device 112. In other examples, thestationary reference unit 106 is provided with audio input portconnectors to interface with audio output ports or speaker outputconnections of the media presentation device 112 via wires to receiveaudio reproduced by the media presentation device 112.

In the illustrated example, the portable meter 108 operates as aportable audio detector that also detects audio emitted from the mediapresentation device 112 in the monitored environment 100. The exampleportable meter 108 digitizes the detected audio and stores time-stampeddigitized audio samples of the detected audio.

In the illustrated example, the processing unit 104 may be configured todetermine monitored distances (d_(M)) between the person 110 and themedia presentation device 112 in real-time or near real-time as theprocessing unit 104 receives audio samples from the stationary referenceunit 106 and the portable meter 108. Additionally or alternatively, theprocessing unit 104 may be configured to determine the monitoreddistances (d_(M)) during a post-process.

In the illustrated example, the example processing unit 104 determineslocations of the person 110 by calculating monitored distances (d_(M))between the person 110 and the media presentation device 112 based ontime-of-flight delays of audio emissions from the media presentationdevice 112. That is, the time-of-flight of audio emissions characterizedby the speed of sound will cause an audio emission from the mediapresentation device 112 to be received at different times by thestationary reference unit 106 and the portable meter 108 when they arelocated at different distances from the media presentation device 112.In the illustrated example, the stationary reference unit 106 and theportable meter 108 timestamp their respective digitized audio samples toindicate times at which they detected corresponding audio. Theprocessing unit 104 subsequently uses such timestamps to determinedelays between the times at which the stationary reference unit 106received audio emissions and the times at which the portable meter 108received the same audio emissions. The processing unit 104 can then usethe measured delays to determine monitored distances (d_(M))corresponding to the locations of the person 110.

The stationary reference unit 106 and the portable meter 108periodically or aperiodically send their time-stamped digitized audiosamples to the processing unit 104. The processing unit 104 of theillustrated example may be implemented in a number of different ways.For example, the processing unit 104 may be implemented by the exampleapparatus 200 of FIG. 2 to determine locations of the person 110 usingtime-domain analyses of the time-stamped digitized audio samples and/orby the example apparatus 300 of FIG. 3 to determine locations of theperson 110 using frequency-domain analyses of the time-stamped digitizedaudio samples.

In the illustrated example, the processing unit 104 includes a neuralnetwork model. Example neural network models are described below inconnection with FIGS. 2 and 3. During installation of the locationdetection system 102 in the monitored environment 100 and prior to use,the processing unit 104 is calibrated with training data associated withknown training distances to train the neural network model to determinelocation information associated with the person 110. In particular,during a calibration process, media content is presented via the mediapresentation device 112 to cause audio emissions therefrom. Throughoutthe collection process, the stationary reference unit 106 detects theaudio emissions and generates corresponding time-stamped digitizedreference audio samples. In addition, the portable meter 108 is moved todifferent locations in the monitored environment 100 shown in FIG. 1 aslocated at known training distances (d_(T1)), (d_(T2)), and (d_(T3))from the media presentation device 112 (and, thus, from the stationaryreference unit 106). At each of the training distances (d_(T1)),(d_(T2)), and (d_(T3)), the portable meter 108 detects the audioemissions from the media presentation device 112 and generatescorresponding time-stamped digitized non-stationary audio samples.

In the illustrated example, the stationary reference unit 106 and themobile meter 108 send their respective time-stamped digitized audiosamples to the processing unit 104 through wireless or wired mediums.The processing unit 104 is also provided with the training distances(d_(T1)), (d_(T2)), and (d_(T3)) and indications of times at which theportable meter 108 was located at the training distances (d_(T1)),(d_(T2)), and (d_(T3)). To train the neural network model to accuratelydetermine locations of the person 110 based on subsequent audiocollection processes, the processing unit 104 provides its neuralnetwork model with a distance value for each training distance (d_(T1)),(d_(T2)), and (d_(T3)) and processes audio sample sets from thestationary reference unit 106 and the portable meter 108 (e.g.,digitized reference and non-stationary audio sample sets collected whenthe portable meter 108 was located at the respective training distances(d_(T1)), (d_(T2)), and (d_(T3))) to provide the neural network withcorrelation coefficients for each of the training distances (d_(T1)),(d_(T2)), and (d_(T3))). In this manner, the neural network model can betrained to correlate the training distances (d_(T1)), (d_(T2)), and(d_(T3)) with the correlation coefficient results associated with thedigitized audio samples to subsequently determine accurate locationinformation when the person 110 is located at different distances fromthe media presentation device 112.

In the illustrated example, location information refers to locations ofthe person 110 relative to the media presentation device 112. That is,the processing unit 104 determines distances separating the person 110from the media presentation device 112 such that the person 110 may belocated at any position along a circumference about the mediapresentation device 112 having a radius equal to the determineddistance. In the illustrated example of FIG. 1, when the processing unit104 subsequently determines that the person 110 was located at amonitored distance (d_(M)) from the media presentation device 112, theperson 110 may have been located anywhere along a circumference 116having a radius equal to the monitored distance (d_(M)) about the mediapresentation device 112.

FIG. 2 depicts an example apparatus 200 that may be used to implementthe example processing unit 104 of FIG. 1 to detect locations ofaudience members in the monitored environment 100 of FIG. 1 based ontime-domain signal analyses. In the illustrated example, the apparatus200 is in communication with the stationary reference unit 106 toreceive digitized reference audio samples 202 and in communication withthe portable meter (PM) 108 to receive digitized non-stationary audiosamples 204. To synchronize clocks in the stationary reference unit 106and the portable meter 108 with one another, the apparatus 200 isprovided with an example time synchronizer 206. In the illustratedexample, the time synchronizer 206 configures the stationary referenceunit 106 and the portable meter 108 to have the same time so thattimestamps of the reference audio samples 202 from the stationaryreference unit 106 correspond to the same timestamps (or substantiallythe same timestamps within an acceptable error tolerance (e.g., +/−number of milliseconds)) of the non-stationary audio samples 204 fromthe portable meter 108. Ensuring that reference audio samples 202 fromthe stationary reference unit 106 correspond in time with respectiveones of the non-stationary audio samples 204 from the portable meter 108facilitates correlation processes performed on the audio samples 202 and204 by the apparatus 200. In the illustrated example, the timesynchronizer 206 can communicate with the stationary reference unit 106and the portable meter 108 through wired or wireless communications toset clocks therein with the same (or substantially the same) time.

In the illustrated example, to sub-sample (or down-sample) the referenceaudio samples 202 from the stationary reference unit 106, the apparatus200 is provided with a sub-sampler 208. In addition, to sub-sample thenon-stationary audio samples 204 from the portable meter 108, theapparatus 200 is provided with a second sub-sampler 210. In theillustrated example, the sub-samplers 208 and 210 reduce the effectivesampling rate at which the received digitized audio samples arerepresented. For example, if the stationary reference unit 106 and theportable meter 108 sample audio at 44 kHz, the sub-samplers 208 and 210may select every other sample of the received digitized audio samples,which results in an effective sampling rate of 22 kHz. Sub-samplingreduces the size of the audio data sets to be processed by the apparatus200, while maintaining the duration or the period over which the sampleswere collected and maintaining sufficient audio quality for determiningaudience member location information. In addition, the sub-samplingadvantageously allows the apparatus 200 to process the digitized audiosamples faster by reducing the amount of data that must be processed. Inthe illustrated example, the sub-samplers 208 and 210 perform 20:1sub-sampling processes to reduce digitized audio sample sets by a factorof 20.

In the illustrated example, to reduce the quantization or digitizationresolution of the non-stationary audio samples 204 from the portablemeter 108, the apparatus 200 is provided with an example down-scaler212. In the illustrated example, the down-scaler 212 is configured toreduce the number of bits used to represent each audio sample of thenon-stationary audio samples 204. In the illustrated example, thedown-scaler 212 down-scales audio samples from 16-bit representations to8-bit representations. The example apparatus 200 is provided with thedown-scaler 212 to normalize the bit-resolutions of the non-stationaryaudio samples 204 from the portable meter 108 to match thebit-resolutions of the reference audio samples 202 from the stationaryreference unit 106, because the stationary reference unit 106 of theillustrated example digitizes audio at the lower, 8-bit quantizationresolution. In other examples in which the portable meter 108 and thestationary reference unit 106 digitize audio using the same bitresolution, the down-scaler 212 is omitted. In yet other examples inwhich the stationary reference unit 106 digitizes audio using a higherbit resolution than the portable meter 108, the down-scaler 212 is usedto down-scale the reference audio samples 202 from the stationaryreference unit 106, rather than the non-stationary audio samples 204from the portable meter 108.

In the illustrated example, to perform correlations between the audiosamples 202 and 204, the apparatus 200 is provided with an examplecorrelator 214. In the illustrated example, the correlator 214determines correlation coefficients (e.g., correlation analysis results)based on correlations between audio sample subsets 402 and 404 (FIGS.4A-4C) generated by the sub-samplers 208 and 210 and the down-scaler 212based on the reference audio samples 202 and the non-stationary audiosamples 204. The correlator 214 of the illustrated example receives asubset of reference audio samples 402 from the sub-sampler 208 and asubset of non-stationary audio samples 404 from the down-scaler 212.

Turning to FIGS. 4A-4C, the correlator 214 aligns the reference audiosample subset 402 with the non-stationary audio sample subset 404 basedon respective timestamps 406 and 408. As shown in FIG. 4A, thetime-aligned subsets 402 and 404 exhibit corresponding similarcharacteristics 410 that are offset from one another by a measureddistance time offset (t_(dist)). In some examples described herein, themeasured distance time offset (t_(dist)) corresponds to the monitoreddistance (d_(M)) between the person 110 and the media presentationdevice 112 shown in FIG. 1.

To determine the measured distance time offset (t_(dist)), thecorrelator 214 determines multiple correlation coefficients byperforming correlations between the reference and non-stationary audiosample subsets 402 and 404. For each correlation coefficientcalculation, the correlator 214 time-shifts the audio sample subsets 402and 404 relative to one another by incremental time shifts to differenttime-shift positions (t_(shift)) as shown in FIGS. 4A, 4B, and 4C suchthat at one of the time-shift positions (t_(shift)) (e.g., the timeshift position (t_(shift)) shown in FIG. 4B) the similar characteristics410 between the audio sample subsets 402 and 404 are close in alignmentor substantially aligned with one another to produce a very strongcorrelation coefficient. In the illustrated examples, the strongestcorrelation between the audio sample subsets 402 and 404 occurs at thetime shift position equal to 2.5 milliseconds (ms) (t_(shift)=2.5 ms) ofFIG. 4B as represented by the notation below the timeline of the showngraph.

In some examples, the correlator 214 initially aligns the referenceaudio sample subset 402 to be negatively shifted in time relative to thenon-stationary audio sample subset 404. The correlator 214 then performsa time-shift sweep of the audio sample subsets 402 and 404 relative toone another when calculating the correlation coefficients such that thetimestamps 406 and 408 of the audio sample subsets 402 and 404 becomematchingly aligned at a subsequent time-shift and, at yet a furthertime-shift, the reference audio sample subset 402 becomes positivelyshifted in time relative to the non-stationary audio sample subset 404.For each time-shift, the correlator 214 associates a correspondingcorrelation coefficient with the corresponding time-shift position(t_(shift)) between the timestamps 406 and 408 of correspondinglyaligned audio samples. For the time-shift at which the similarcharacteristics 408 are aligned with one another, as indicated by thevery strongest correlation coefficient, the measured distance timeoffset (t_(dist)) (e.g., the measured distance time offset (t_(dist))shown in FIG. 4A) is substantially equal to the time-shift position(t_(shift)) of that time shift.

Although only three time-shifts are shown in FIGS. 4A-4C, in theillustrated example, the correlator 214 determines correlationcoefficients for the audio sample subsets 402 and 404 based on moretime-shifts. In addition, the time-shifts may be based on finer orcoarser time steps (e.g., 0.2 ms increments, 0.5 ms increments, 1 msincrements, 2 ms increments, 5 ms increments, etc.).

In the illustrated example, to determine correlation coefficients basedon time-domain analyses, the correlator 214 may elect to use one ofEquation 1 or Equation 2 below based on whether the average of one orboth of the audio samples 202 and 204 is equal to zero (i.e., whetherthe audio samples 202 and 204 have DC biases). In the illustratedexample, the average of an audio sample set is equal to zero when itdoes not have a direct current (DC) bias. Such a DC bias may beeliminated from an audio signal using a high-pass filter or any othersuitable filtering process such as, for example, a moving averageprocess. For example, one or both of the stationary reference unit 106and/or the portable meter 108 may be provided with a respectivehigh-pass filter (or any other suitable filter) to remove DC bias from adetected audio signal prior to digitizing the signal to produce theaudio samples 202 and/or 204.

In the illustrated example, the correlator 214 uses Equation 1 below toperform a correlation between the reference and non-stationary audiosamples 202 and 204 and determine a correlation coefficient (r) when theaverages of both of the audio samples 202 and 204 are equal to zero(i.e., neither of the audio samples 202 and 204 contain a DC bias). Insuch instances, the stationary reference unit 106 and the portable meter108 used high-pass filters (or other suitable filters to remove DCbiases) to filter audio signals corresponding to the reference andnon-stationary audio samples 202 and 204.

$\begin{matrix}{r = \frac{\sum{xy}}{\sqrt{\left( {\sum x^{2}} \right)\left( {\sum y^{2}} \right)}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$Using Equation 1 above, the correlator 214 determines a correlationcoefficient (r) (e.g., a correlation analysis result) by performingthree summations (Σxy, Σx², Σy²) based on the reference andnon-stationary audio samples 202 and 204. An example process that may beused by the correlator 214 to determine correlation coefficients (r)based on Equation 1 is described below in connection with the exampleflow diagram of FIG. 7.

In the illustrated example, the correlator 214 uses Equation 2 below toperform a correlation between the reference and non-stationary audiosamples 202 and 204 and determine a correlation coefficient (r) when theaverage of the reference audio samples 202 is zero (i.e., the referenceaudio samples 202 have no DC bias) and the average of the non-stationaryaudio samples 204 is not equal to zero (i.e., the non-stationary audiosamples 204 have a DC bias). In such instances, the stationary referenceunit 106 used a high-pass filter (or other suitable filter to remove aDC bias) to filter an audio signal corresponding to the reference audiosamples 202.

$\begin{matrix}{r = \frac{n{\sum{xy}}}{\sqrt{n\left( {\sum x^{2}} \right)}\sqrt{{n\left( {\sum y^{2}} \right)} - \left( {\sum y} \right)^{2}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$Using Equation 2 above, the correlator 214 determines a correlationcoefficient (r) (e.g., a correlation analysis result) using foursummations (Σxy, Σx², Σy², Σy) based on the reference and non-stationaryaudio samples 202 and 204. An example process that may be used by thecorrelator 214 to determine correlation coefficients (r) based onEquation 2 is described below in connection with the example flowdiagram of FIGS. 8A and 8B.

In the illustrated example, Equations 1 and 2 may be derived from knownEquation 3 below when the respective conditions noted above regarding DCbiases of the reference and non-stationary audio samples 202 and 204 aretrue.

$\begin{matrix}{r = \frac{{n{\sum{xy}}} - {\left( {\sum x} \right)\left( {\sum y} \right)}}{\sqrt{{n\left( {\sum x^{2}} \right)} - \left( {\sum x} \right)^{2}}\sqrt{{n\left( {\sum y^{2}} \right)} - \left( {\sum y} \right)^{2}}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$In some examples, Equation 3 may be used to determine correlationcoefficients (r) (e.g., a correlation analysis results) when it isunknown whether a DC bias is present in either of the audio samples 202and 204 and/or when DC biases are present in both of the audio samples202 and 204.

As shown in FIG. 2, to analyze and select ones of the correlationcoefficients (r) determined using Equation 1, Equation 2, and/orEquation 3 above, the apparatus 200 is provided with a correlationanalyzer 216. In the illustrated example, the correlation analyzer 216receives correlation coefficients (r) and corresponding time-shiftpositions (t_(shift)) determined by the correlator 214 based on multipletime shifts of the reference and non-stationary audio sample subsets 402and 404 of FIGS. 4A-4C. In the illustrated example, the correlationanalyzer 216 compares the correlation coefficients (r) to one anotherand selects the three maximum or peak coefficients (r_(max)) and threeminimum or valley coefficients (r_(min)). In the illustrated example,the three maximum coefficients (r_(max)) are coefficients that have thelargest values relative to the other correlation coefficients (r) forcorresponding reference and non-stationary audio sample subsets 402 and404, and the three minimum coefficients (r_(min)) are coefficients thathave the smallest values relative to the other correlation coefficients(r) for corresponding reference and non-stationary audio sample subsets402 and 404. In other examples, fewer or more maximum coefficients(r_(max)) and minimum coefficients (r_(min)) may be selected.

In the illustrated example of FIG. 2, to select a maximum-amplitudeaudio sample from the reference audio samples 202, the apparatus 200 isprovided with an example maximum level detector 218. The example maximumlevel detector 218 selects an audio sample having the largest amplitudefrom the reference audio samples 202 over a time period of interest andstores a value corresponding to its amplitude. In addition, to select amaximum-amplitude audio sample from the non-stationary audio samples204, the apparatus 200 is provided with another example maximum leveldetector 220. The example maximum level detector 220 selects an audiosample having the largest amplitude from the non-stationary audiosamples 204 over a time period of interest and stores a valuecorresponding to its amplitude.

In the illustrated example of FIG. 2, to determine the monitoreddistance (d_(M)) of the person 110 from the media presentation device112 of FIG. 1, the apparatus 200 is provided with a neural network 222.The neural network 222 receives the three maximum coefficients(r_(max)), the three minimum coefficients (r_(min)), and correspondingtime-shift positions (t_(shift)) from the correlation analyzer 216. Inaddition, the neural network 222 receives a maximum-amplitude referenceaudio sample value from the maximum level detector 218 and amaximum-amplitude non-stationary audio sample value from the maximumlevel detector 220. In the illustrated example, the neural network 222implements a neural network model that is previously trained based onthe known training distances (d_(T1)), (d_(T2)), and (d_(T3)) asdiscussed above in connection with FIG. 1 to generate distance measuressuch as the monitored distance (d_(M)). Thus, in the illustratedexample, the neural network 222 applies its previously trained neuralnetwork model to the three maximum coefficients (r_(max)), the threeminimum coefficients (r_(min)), corresponding time-shift positions(t_(shift)), the maximum-amplitude reference audio sample value, and themaximum-amplitude non-stationary audio sample value to determine adistance result 224 (e.g., the monitored distance (d_(M)) of FIG. 1).

FIG. 3 depicts another example apparatus 300 that may be used toimplement the example processing unit 104 to detect locations ofaudience members in the monitored environment 100 of FIG. 1 based onfrequency-domain analyses. In the illustrated example, the apparatus 300is in communication with the stationary reference unit 106 to receivethe digitized reference audio samples 202 and in communication with theportable meter (PM) 108 to receive the digitized non-stationary audiosamples 204. To synchronize clocks in the stationary reference unit 106and the portable meter 108 with one another as discussed above inconnection with FIG. 2, the apparatus 300 is provided with an exampletime synchronizer 306, which is substantially similar or identical tothe time synchronizer 206 of FIG. 2.

In the illustrated example, to convert the reference audio samples 202from the time domain to the frequency domain, the apparatus 300 isprovided with an example frequency-domain transformer 308. In addition,to convert the non-stationary audio samples 204 to the frequency domain,the apparatus 300 is provided with another example frequency-domaintransformer 310. In the illustrated example, the frequency-domaintransformers 308 and 310 determine fast Fourier transforms (FFTs) ofaudio samples to enable frequency domain analyses of the same.

In the illustrated example, the apparatus 300 is not provided withsub-samplers (e.g., the sub-samplers 208 and 210 of FIG. 2). The exampleapparatus 300 can perform frequency domain analyses based on a fullaudio sample sets collected during a shorter duration by the stationaryreference unit 106 and the portable unit 108, whereas the apparatus 200performs time-domain analyses on audio sample sets collected duringlonger durations and uses the sub-samplers 208 and 210 to reduce thesize of the audio sample sets. In the illustrated examples disclosedherein, the quantity of audio samples collected by each of thestationary reference unit 106 and the portable meter 108 is based on thedesired size of a correlation window and time-shift range fordetermining correlation coefficients (r). In examples using the exampleapparatus 200 of FIG. 2 to perform time-domain analyses, each of thestationary reference unit 106 and the portable meter 108 may collect 750ms of audio samples plus an additional quantity of audio samples equalto twice the duration of the desired time-shift range. In such examples,the sub-samplers 208 and 210 are then used to reduce quantities ofsamples while maintaining the duration over which the audio samples wereacquired. In other examples using the example apparatus 300 of FIG. 3 toperform frequency-domain analyses, each of the stationary reference unit106 and the portable meter 108 may collect 32 ms of audio samples plusan additional quantity of audio samples equal to twice the duration ofthe desired time-shift range. The example apparatus 300 does not employsub-sampling of the acquired audio samples for frequency-domainanalyses, because the number of audio samples acquired during therelatively shorter duration is already of a smaller, more manageablequantity compared to the number of audio samples collected by thestationary reference unit 106 and the portable meter 108 for time-domainanalyses. Frequency-domain analyses may be advantageously used toimprove battery charge performance of the portable meter 108, becauseless audio samples are acquired for frequency-domain analyses to achievesimilar results as those produced using time-domain analyses.

Referring again to FIG. 3, to perform correlations betweenfrequency-domain representations of the audio samples 202 and 204, theapparatus 300 is provided with an example correlator 314. In theillustrated example, the frequency-domain transformers 308 and 310 andthe correlator 314 work cooperatively to frequency-correlate multipletime-shifted instances of the reference and non-stationary audio samples202 and 204. In this manner, the correlator 314 may determine multiplecorrelation coefficients, each corresponding to a different time-shiftedinstance of the audio samples 202 and 204. For example, turning brieflyto FIG. 5, the frequency domain transformer 308 is shown as generatingmultiple reference frequency transforms 502, each corresponding to arespective one of a 0 ms time-shift position (t_(shift)=0 ms), a 2.5 mstime-shift position (t_(shift)=2.5 ms), and a 3.5 ms time-shift position(t_(shift)=3.5 ms). In addition, the frequency-domain transformer 310 isshown as generating multiple non-stationary frequency transforms 504,each corresponding to a respective one of the 0 ms time-shift position(t_(shift)=0 ms), the 2.5 ms time-shift position (t_(shift)=2.5 ms), andthe 3.5 ms time-shift position (t_(shift)=3.5 ms). For thefrequency-domain transforms performed by the frequency-domaintransformers 308 and 310, the time shifting is performed in similarfashion as described above in connection with FIGS. 4A-4C. Thus, thetime shift positions (t_(shift)) of the audio samples 202 and 204 aresubstantially similar or identical to the time shift positions shown inFIGS. 4A-4C. Although three time shifts are noted (t_(shift)=0 ms,t_(shift)=2.5 ms, and t_(shift)=3.5 ms), the correlator 314 maydetermine correlation coefficients (r) for any other quantity of timeshifts.

In the illustrated example of FIG. 5, for each time-shift position(t_(shift)), the correlator 314 determines frequency-based correlationcoefficients (r) 506 for the multiple frequency-domain representations502 and 504. In the illustrated example, the correlator 314 may useEquation 4 or Equation 5 below to determine the frequency-basedcorrelation coefficients (r) 506. In particular, the correlator 314 mayelect to use one of Equation 4 or Equation 5 below based on whether theaverage of one or both of the audio samples 202 and 204 is equal to zero(i.e., whether either or both of the audio samples 202 and 204 have DCbiases). In the illustrated example, the average of an audio sample setis equal to zero when it does not have a direct current (DC) bias. Sucha DC bias may be eliminated from an audio signal using a high-passfilter or any other suitable filtering process such as, for example, amoving average process. For example, one or both of the stationaryreference unit 106 and/or the portable meter 108 may be provided with arespective high-pass filter (or any other suitable filter) to remove DCbias from a detected audio signal prior to digitizing the signal toproduce the audio samples 202 and/or 204.

In the illustrated example, the correlator 314 uses Equation 4 below toperform frequency-based correlations between different time-shiftposition (t_(shift)) instances of the reference and non-stationary audiosamples 202, 204 and to determine respective correlation coefficients(r) when the averages of both of the audio samples 202 and 204 are equalto zero (i.e., neither of the audio samples 202 and 204 contains a DCbias). In such instances, the stationary reference unit 106 and theportable meter 108 used high-pass filters (or other suitable filters toremove DC biases) to filter audio signals corresponding to the referenceand non-stationary audio samples 202 and 204.

$\begin{matrix}{r = \frac{\sum{XY}^{*}}{\sqrt{\left( {\sum{XX}^{*}} \right) - \left( {\sum{YY}^{*}} \right)}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$Using Equation 4 above, the correlator 314 determines a correlationcoefficient (r) (e.g., a correlation analysis result) by performingthree summations (ΣXY*, ΣXX*, ΣYY*) based on the complex conjugate pairs(X,X*) and (Y,Y*) of frequency-domain representations 502 and 504 (FIG.5) of the reference and non-stationary audio samples 202 and 204. Anexample process that may be used by the correlator 314 of FIG. 3 todetermine correlation coefficients (r) based on Equation 4 is describedbelow in connection with the example flow diagram of FIG. 9.

In the illustrated example, the correlator 314 uses Equation 5 below toperform frequency-based correlations between different time-shiftposition (t_(shift)) instances of the reference and non-stationary audiosamples 202, 204 and to determine respective correlation coefficients(r) when the average of the reference audio samples 202 is zero (i.e.,the reference audio samples 202 have no DC bias), but the average of thenon-stationary audio samples 204 is not equal to zero (i.e., thenon-stationary audio samples 204 have a DC bias). In such instances, thestationary reference unit 106 used a high-pass filter (or other suitablefilter to remove a DC bias) to filter an audio signal corresponding tothe reference audio samples 202.

$\begin{matrix}{r = \frac{n{\sum{XY}^{*}}}{\sqrt{n\left( {\sum{XX}^{*}} \right)}\sqrt{{n\left( {\sum{YY}^{*}} \right)} - {\left( {\sum Y} \right)\left( {\sum Y} \right)^{*}}}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$Using Equation 5 above, the correlator 314 determines a correlationcoefficient (r) (e.g., a correlation analysis result) using foursummations (ΣXY*, ΣXX*, ΣYY*, ΣY) based on the complex conjugate pairs(X,X*) and (Y,Y*) of frequency-domain representations 502 and 504 (FIG.5) of the reference and non-stationary audio samples 202 and 204. Anexample process that may be used by the correlator 314 of FIG. 3 todetermine correlation coefficients (r) based on Equation 5 is describedbelow in connection with the example flow diagram of FIGS. 10A-10C.

In the illustrated example, Equations 4 and 5 may be derived fromEquation 6 below when the respective conditions noted above regarding DCbiases of the reference and non-stationary audio samples 202 and 204 aretrue.

$\begin{matrix}{r = \frac{{n{\sum\left( {XY}^{*} \right)}} - {\left( {\sum X} \right)\left( {\sum Y^{*}} \right)}}{\begin{matrix}{\sqrt{n\left( {\sum{XX}^{*}} \right)}\sqrt{\left( {\sum X} \right)\left( {\sum X} \right)^{*}}} \\\sqrt{{n\left( {\sum{YY}^{*}} \right)} - {\left( {\sum Y} \right)\left( {\sum Y} \right)^{*}}}\end{matrix}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$In some example implementations, Equation 6 may be used to determinecorrelation coefficients (r) (e.g., correlation analysis results) whenit is unknown whether a DC bias is present in either of the audiosamples 202 and 204 and/or when DC biases are present in both of theaudio samples 202 and 204. An example process that may be used by thecorrelator 314 of FIG. 3 to determine correlation coefficients (r) basedon Equation 6 is described below in connection with the example flowdiagram of FIGS. 11A-11C.

For frequency-domain analyses using Equations 4-6 above, the correlationcoefficients (r) may be represented in polar coordinate notation (i.e.,r=ρ∠θ), in which the correlation coefficients (r) are expressed as amagnitude (ρ) at a phase difference (θ) between the two audio signals.In some examples in which phase distortion between audio signals issmall (e.g., minimal) or can otherwise be ignored (e.g., there is nointerest in taking phase distortion into account or no interest inanalyzing phase delay), the correlation coefficients (r) may berepresented using the magnitude (ρ) without the phase difference (θ).

As shown in FIG. 3, to analyze and select ones of the correlationcoefficients (r) determined using Equation 4, Equation 5, and/orEquation 6 above, the apparatus 300 is provided with a correlationanalyzer 316. In the illustrated example, the correlation analyzer 316receives correlation coefficients (r) and corresponding time-shiftpositions (t_(shift)) from the correlator 314 based on multiple timeshifts of the reference and non-stationary audio samples 202 and 204(e.g., similar to time-shift positions (t_(shift)) of the reference andnon-stationary audio sample subsets 402 and 404 of FIGS. 4A-4C). In theillustrated example, the correlation analyzer 316 compares thecorrelation coefficients (r) to one another and selects the six maximumor peak coefficients (r_(max)). In the illustrated example, the sixmaximum coefficients (r_(max)) are coefficients that have the largestvalues relative to the other correlation coefficients (r) forcorresponding reference and non-stationary audio samples 202 and 204. Inother examples, fewer or more maximum coefficients (r_(max)) may beselected.

In the illustrated example of FIG. 3, to select a maximum-amplitudeaudio sample from the reference audio samples 202, the apparatus 300 isprovided with an example maximum level detector 318. The example maximumlevel detector 318 selects an audio sample having the largest amplitudefrom the reference audio samples 202 over a time period of interest andstores a value corresponding to its amplitude. In addition, to select amaximum-amplitude audio sample from the non-stationary audio samples204, the apparatus 300 is provided with another example maximum leveldetector 320. The example maximum level detector 320 selects an audiosample having the largest amplitude from the non-stationary audiosamples 204 over a time period of interest and stores a valuecorresponding to its amplitude.

In the illustrated example of FIG. 3, to determine the monitoreddistance (d_(M)) of the person 110 of FIG. 1, the apparatus 300 isprovided with a neural network 322. The neural network 322 receives thesix maximum coefficients (r_(max)) and corresponding time-shiftpositions (t_(shift)) from the correlation analyzer 316. In addition,the neural network 322 receives a maximum-amplitude reference audiosample value from the maximum level detector 318 and a maximum-amplitudenon-stationary audio sample value from the maximum level detector 320.In the illustrated example, the neural network 322 implements a neuralnetwork model that is previously trained based on the known trainingdistances (d_(T1)), (d_(T2)), and (d_(T3)) as discussed above inconnection with FIG. 1 to generate distance measures such as themonitored distance (d_(M)). Thus, in the illustrated example, the neuralnetwork 322 applies its previously trained neural network model to thesix maximum coefficients (r_(max)), corresponding time-shift positions(t_(shift)), the maximum-amplitude reference audio sample value, and themaximum-amplitude non-stationary audio sample value to determine adistance result 324 (e.g., the monitored distance (d_(M)) of FIG. 1).

In some examples, the apparatus 200 of FIG. 2 and the apparatus 300 ofFIG. 3 are both implemented in the processing unit 104 of FIG. 1. Insuch examples, the processing unit 104 is configured to determine whichof the apparatus 200 or the apparatus 300 it should use based on whetherit is to perform a time-domain analysis or a frequency-domain analysisof received audio samples (e.g., the audio samples 202 and 204 of FIGS.2 and 3). For example, user-defined configurations may be set in theprocessing unit 104 to select between performance of time-domain orfrequency-domain analyses based on any number and/or types of factors.Such factors may include the acoustic characteristics of the monitoredenvironment 100, the size of the monitored environment 100, the devicetype of the media presentation device 112 (e.g., a stereo, a computer, atelevision, etc.), the speaker type of the media presentation device 112(e.g., mixed range speakers, high-fidelity speakers, low-cost speakers,etc.), the processing power of the processing unit 104, the processingpower of the stationary reference unit 106, the processing power of theportable meter 108, the battery capacity or other batterycharacteristics of the portable unit 108, etc.

In other examples, the processing unit 104 may be provided with only oneof the apparatus 200 or the apparatus 300. In such examples, theprocessing unit 104 is configured to perform only time-domain analysesor perform only frequency-domain analyses.

While example manners of implementing the apparatus 200 and 300 havebeen illustrated in FIGS. 2 and 3, one or more of the elements,processes and/or devices illustrated in FIGS. 2 and 3 may be combined,divided, re-arranged, omitted, eliminated and/or implemented in anyother way. Further, the time synchronizer 206, the sub-samplers 208 and210, the down-scaler 212, the correlation analyzer 216, the neuralnetwork 222 and/or, more generally, the example apparatus 200 of FIG. 2may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. In addition, the timesynchronizer 306, the frequency-domain transformers 308 and 310, thecorrelator 314, the correlation analyzer 316, the neural network 322and/or, more generally, the example apparatus 300 of FIG. 3 may beimplemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of the timesynchronizer 206, the sub-samplers 208 and 210, the down-scaler 212, thecorrelation analyzer 216, the neural network 222, the example apparatus200, the time synchronizer 306, the frequency-domain transformers 308and 310, the correlator 314, the correlation analyzer 316, the neuralnetwork 322 and/or the example apparatus 300 could be implemented by oneor more circuit(s), programmable processor(s), application specificintegrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s))and/or field programmable logic device(s) (FPLD(s)), etc. When any ofthe appended apparatus claims are read to cover a purely software and/orfirmware implementation, at least one of the time synchronizer 206, thesub-samplers 208 and 210, the down-scaler 212, the correlation analyzer216, the neural network 222, the time synchronizer 306, thefrequency-domain transformers 308 and 310, the correlator 314, thecorrelation analyzer 316, and/or the neural network 322 are herebyexpressly defined to include a computer readable medium such as amemory, DVD, CD, etc. storing the software and/or firmware. Furtherstill, the example apparatus 200 and 300 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIGS. 2 and 3, and/or may include more than one of any orall of the illustrated elements, processes and devices.

FIGS. 6A, 6B, 7, 8A, 8B, 9, 10A-10C, and 11A-11C are flow diagramsrepresentative of example machine readable instructions that can beexecuted to implement example methods, apparatus, and systems describedherein. The example processes of FIGS. 6A, 6B, 7, 8A, 8B, 9, 10A-10C,and 11A-11C may be implemented using machine readable instructions that,when executed, cause a device (e.g., a programmable controller or otherprogrammable machine or integrated circuit) to perform the operationsshown in FIGS. 6A, 6B, 7, 8A, 8B, 9, 10A-10C, and 11A-11C. For instance,the example processes of FIGS. 6A, 6B, 7, 8A, 8B, 9, 10A-10C, and11A-11C may be performed using a processor, a controller, and/or anyother suitable processing device. For example, the example processes ofFIGS. 6A, 6B, 7, 8A, 8B, 9, 10A-10C, and 11A-11C may be implementedusing coded instructions stored on a tangible machine readable mediumsuch as a flash memory, a read-only memory (ROM), and/or a random-accessmemory (RAM).

As used herein, the term tangible computer readable medium is expresslydefined to include any type of computer readable storage and to excludepropagating signals. Additionally or alternatively, the exampleprocesses of FIGS. 6A, 6B, 7, 8A, 8B, 9, 10A-10C, and 11A-11C may beimplemented using coded instructions (e.g., computer readableinstructions) stored on a non-transitory computer readable medium suchas a flash memory, a read-only memory (ROM), a random-access memory(RAM), a cache, or any other storage media in which information isstored for any duration (e.g., for extended time periods, permanently,brief instances, for temporarily buffering, and/or for caching of theinformation). As used herein, the term non-transitory computer readablemedium is expressly defined to include any type of computer readablemedium and to exclude propagating signals.

Alternatively, the example processes of FIGS. 6A, 6B, 7, 8A, 8B, 9,10A-10C, and 11A-11C may be implemented using any combination(s) ofapplication specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)), field programmable logic device(s) (FPLD(s)),discrete logic, hardware, firmware, etc. Also, the example processes ofFIGS. 6A, 6B, 7, 8A, 8B, 9, 10A-10C, and 11A-11C may be implemented asany combination(s) of any of the foregoing techniques, for example, anycombination of firmware, software, discrete logic and/or hardware.

Although the example processes of FIGS. 6A, 6B, 7, 8A, 8B, 9, 10A-10C,and 11A-11C are described with reference to the flow diagrams of FIGS.6A, 6B, 7, 8A, 8B, 9, 10A-10C, and 11A-11C, other methods ofimplementing the processes of FIGS. 6A, 6B, 7, 8A, 8B, 9, 10A-10C, and11A-11C may be employed. For example, the order of execution of theblocks may be changed, and/or some of the blocks described may bechanged, eliminated, sub-divided, or combined. Additionally, one or bothof the example processes of FIGS. 6A, 6B, 7, 8A, 8B, 9, 10A-10C, and11A-11C may be performed sequentially and/or in parallel by, forexample, separate processing threads, processors, devices, discretelogic, circuits, etc.

Turning in detail to FIGS. 6A and 6B, the depicted example process maybe used to determine locations of audience members (e.g., the person 110of FIG. 1). Initially, a neural network for use in determining audiencemember locations is trained (block 602). For example, the apparatus 200of FIG. 2 may train its neural network 222 for use in determiningaudience member locations based on time-domain analyses of the referenceand non-stationary audio samples 202 and 204 and/or the apparatus 300 ofFIG. 3 may train its neural network 322 for use in determining audiencemember locations based on frequency-domain analyses of the reference andnon-stationary audio samples 202 and 204. In either instance, during atraining process, the apparatus 200 and/or the apparatus 300 is/areprovided with multiple sets of reference and non-stationary audiosamples from the stationary reference unit 106 and the portable meter108 corresponding to the training distances (d_(T1), d_(T2), d_(T3))along with the distance values of the training distances (d_(T1),d_(T2), d_(T3)) corresponding to each audio sample set. In this manner,the apparatus 200 and/or the apparatus 300 can train its respectiveneural network 222 and/or 322 by inputting resulting correlationcoefficients (r) and the distance values of the training distances(d_(T1), d_(T2), d_(T3)) using any suitable neural network trainingtechnique so that the neural network(s) 222 and/or 322 can configureits/their logic to determine distance measures (e.g., the monitoreddistance d_(M) of FIG. 1) based on subsequent reference andnon-stationary audio samples 202 and 204 collected by the stationaryreference unit 106 and the portable meter 108.

After the neural network training phase of block 602, the subsequentoperations of the flow diagram of FIGS. 6A and 6B correspond to amonitoring phase in which the apparatus 200 and/or the apparatus 300received reference and non-stationary audio samples (e.g., the referenceand non-stationary audio samples 202 and 204 of FIGS. 2 and 3) todetermine monitored distances (d_(M)) of the person 110 of FIG. 1.

Initially, during a monitoring phase, the processing unit 104 determineswhether it should perform a time-domain analysis (block 604) using theapparatus 200 of FIG. 2 to determine the monitored distance (d_(M)) ofthe person 110. In some examples, the processing unit 104 may bepre-programmed or configured by a user (e.g., an installationtechnician) to always perform time-domain analyses or to always performfrequency-domain analyses. Such configuration may be implemented bysetting a software register option (e.g., a flag or bit) in theprocessing unit 104 or by setting a hardware switch on the processingunit 104 specifying which type of analysis to perform. In instances inwhich the processing unit 104 is only provided with the exampleapparatus 200 of FIG. 2 to perform time-domain analyses or is onlyprovided with the example apparatus 300 of FIG. 3 to performfrequency-domain analyses, the decision of block 604 may be omitted.That is, in instances in which the processing unit 104 is only providedwith the apparatus 200 to perform time-domain analyses, the processingunit 104 performs the operations of blocks 604, 608, 610, 612, 614, 616,618, 620, and 622 of FIG. 6A (without needing to perform the decision ofblock 604) to determine monitored distances (d_(M)) of the person 110after a neural network training phase in which the neural network 222(FIG. 2) is trained. Whereas, in instances in which the processing unit104 is only provided with the apparatus 300 to perform frequency-domainanalyses, the processing unit 104 performs the operations shown in FIG.6B (without needing to perform the decision of block 604) to determinemonitored distances (d_(M)) of the person 110 after a neural networktraining phase in which the neural network 322 (FIG. 3) is trained.

If the processing unit 104 determines at block 604 that it should use atime-domain analysis, the processing unit 104 of FIG. 1 receives thereference and non-stationary audio samples 202 and 204 (block 606). Inthe illustrated example, the quantity of audio samples collected by eachof the stationary reference unit 106 and the portable meter 108 is basedon the desired size of the correlation window and time-shift range fordetermining the correlation coefficients (r). In the illustratedexample, for time-domain analyses, each of the stationary reference unit106 and the portable meter 108 may collect 750 ms of audio samples plusan additional quantity of audio samples equal to twice the duration ofthe desired time-shift range.

The sub-samplers 208 and 210 of FIG. 2 perform sub-sampling operationson respective ones of the reference and non-stationary audio samples 202and 204 (block 608) to generate the reference and audio sample subsets402 and 404 of FIGS. 4A-4C. The down-scaler 212 of FIG. 2 down-scalesthe non-stationary audio sample subset 404 (block 610). The correlator214 (FIG. 2) then determines correlation coefficients (r) (e.g.,correlation analysis results) (block 612) based on the reference andaudio sample subsets 402 and 404. For instances in which neither of thereference and non-stationary audio samples 202 and 204 have a DC bias,the correlator 214 may perform the operation of block 612 using Equation1 above. An example process that may be used to implement Equation 1 isdescribed below in connection with the flow diagram of FIG. 7.Alternatively, if the reference audio samples 202 do not have a DC bias,but the non-stationary audio samples 204 do have a DC bias, thecorrelator 214 may perform the operation of block 612 using Equation 2above. An example process that may be used to implement Equation 2 isdescribed below in connection with the flow diagram of FIGS. 8A and 8B.

The processing unit 104 then selects three maximum correlationcoefficients (r_(max)) and three minimum correlation coefficients(r_(min)) and corresponding time shift positions (t_(shift)) (block614). For example, the processing unit 104 uses the correlation analyzer216 of FIG. 2 to perform the operation of block 614 for the time-domainanalysis. The processing unit 104 then determines the maximum-amplitudevalues of the reference and non-stationary audio samples 202 and 204(block 616). For example, the processing unit 104 uses the maximum leveldetectors 218 and 220 of FIG. 2 to perform the operation of block 616for the time-domain analysis.

The processing unit 104 then determines the monitored distance (d_(M))(block 618) based on the three maximum correlation coefficients(r_(max)) the three minimum correlation coefficients (r_(min)), thecorresponding time shift positions (t_(shift)), and themaximum-amplitude values of the reference and non-stationary audiosamples 202 and 204. For example, the processing unit 104 uses theneural network 222 of FIG. 2 to perform the operation of block 618 forthe time-domain analysis.

The processing unit 104 determines the location of the person 110 in themonitored environment 100 of FIG. 1 based on the monitored distance(d_(M)) (block 620). For example, the processing unit 104 may determinethat the location of the person 110 is along the circumference 116 aboutthe media presentation device 112 having a radius equal to the monitoreddistance (d_(M)).

In the illustrated example, the location of the person 110 issubsequently used to selectively credit media content (block 622)presented via the media presentation device 112. For example, theprocessing unit 104 may elect to award exposure credit to media contentas having been exposed to the person 110 if the person 110 was within athreshold distance (e.g., 10 feet) of the media presentation device 112.Alternatively, the processing unit 104 may elect to withhold creditingthe media content with an exposure credit if the determined locationindicates that the person 110 was not within a threshold distance (e.g.,10 feet) of the media presentation device 112 to have been adequatelyexposed to the media content. The threshold distance is selected to beequal to a distance that is sufficiently proximate the mediapresentation device 112 to assure the person was exposed to the program.The distance selected may be dependent on the environment of use. Theexample process of FIG. 6A then ends.

Turning now to FIG. 6B, for instances in which the processing unit 104determines at block 604 (FIG. 6A) that it should perform afrequency-domain analysis (i.e., it should not perform a time-domainanalysis), the processing unit 104 of FIG. 1 receives the reference andnon-stationary audio samples 202 and 204 (block 624). In the illustratedexample, the quantity of audio samples collected by each of thestationary reference unit 106 and the portable meter 108 is based on thedesired size of the correlation window and time-shift range fordetermining the correlation coefficients (r). In the illustratedexample, to perform the frequency-domain analysis, each of thestationary reference unit 106 and the portable meter 108 may collect 32ms of audio samples plus an additional quantity of audio samples equalto twice the duration of the desired time-shift range. Frequency-domainanalyses may be advantageously used to improve battery chargeperformance of the portable meter 108, because less audio samples arerequired under frequency-domain analyses to achieve similar results asthose produced using time-domain analyses.

The processing unit 104 uses the apparatus 300 of FIG. 3 to determinethe correlation coefficients (e.g., correlation analysis results) atblock 626. In such instances, the correlator 314 uses Equation 4 aboveto determine the correlation coefficients (r) at block 626 when neitherof the reference and non-stationary audio samples 202 and 204 have a DCbias. An example process that may be used to implement Equation 4 isdescribed below in connection with the flow diagram of FIG. 9.Alternatively, if the reference audio samples 202 do not have a DC bias,but the non-stationary audio samples 204 do have a DC bias, thecorrelator 314 performs the operation of block 626 using Equation 5above. An example process that may be used to implement Equation 5 isdescribed below in connection with the flow diagram of FIGS. 10A-10C.Alternatively if both of the reference and non-stationary audio samples202 and 204 have a DC bias or if it is not known whether either of thereference and non-stationary audio samples 202 and 204 have DC biases,the correlator 314 performs the operation of block 626 using Equation 6as described below in connection with the flow diagram of FIGS. 11A-11C.

The processing unit 104 then selects six maximum correlationcoefficients (r_(max)) and corresponding time shift positions(t_(shift)) (block 628). In the illustrated example, the processing unit104 uses the correlation analyzer 316 of FIG. 3 to perform the operationof block 628 for the frequency-domain analysis. The processing unit 104then determines the maximum-amplitude values of the reference andnon-stationary audio samples 202 and 204 (block 630). In the illustratedexample, the processing unit 104 uses the maximum level detectors 318and 320 of FIG. 3 to perform the operation of block 630 for thefrequency-domain analysis.

The processing unit 104 then determines the monitored distance (d_(M))(block 632) based on the six maximum correlation coefficients (r_(max)),the corresponding time shift positions (t_(shift)), and themaximum-amplitude values of the reference and non-stationary audiosamples 202 and 204. In the illustrated example, the processing unit 104uses the neural network 322 of FIG. 3 to perform the operation of block618 for the frequency-domain analysis.

The processing unit 104 determines the location of the person 110 in themonitored environment 100 of FIG. 1 based on the monitored distance(d_(M)) (block 634). For example, the processing unit 104 may determinethat the location of the person 110 is along the circumference 116 aboutthe media presentation device 112 having a radius equal to the monitoreddistance (d_(M)).

In the illustrated example, the location of the person 110 issubsequently used to selectively credit media content (block 636)presented via the media presentation device 112. For example, theprocessing unit 104 may elect to award exposure credit to media contentas having been exposed to the person 110 if the person 110 was within athreshold distance (e.g., 10 feet) of the media presentation device 112.Alternatively, the processing unit 104 may elect to withhold creditingthe media content with an exposure credit if the determined locationindicates that the person 110 was not within a threshold distance (e.g.,10 feet) of the media presentation device 112 to have been adequatelyexposed to the media content. The threshold distance is selected to beequal to a distance that is sufficiently proximate the mediapresentation device 112 to assure the person was exposed to the program.The distance selected may be dependent on the environment of use. Theexample process of FIG. 6B then ends.

Turning now to FIG. 7, the depicted example flow diagram isrepresentative of an example process that may be used by the correlator214 of FIG. 2 to determine correlation coefficients (r) based onEquation 1 above. In the illustrated example, the example process ofFIG. 7 may be used to implement the operation of block 612 of FIG. 6A.

Initially, the correlator 214 determines a sum of squared referenceaudio samples (Σx²) (block 702) based on the square of each audio samplein the reference audio samples 202. The correlator 214 also determines asum of squared non-stationary audio samples (Σy²) (block 704) based onthe square of each audio sample in the non-stationary audio samples 204.The correlator 214 multiplies the sums of squares (Σx²) and (Σy²) todetermine a product ((Σx²)(Σy²)) (block 706) and determines the squareroot of the product (√{square root over ((Σx²)(Σy²))}{square root over((Σx²)(Σy²))}) (block 708). The correlator 214 determines a sum ofcross-sample products (Σxy) (block 710) based on products (xy) of eachreference audio sample 202 (x) and a corresponding one of thenon-stationary audio samples 204 (y). The correlator 214 then determinesthe correlation coefficient (r) associated with the reference andnon-stationary audio samples 202 and 204 by dividing the sum ofcross-sample products (Σxy) by the square root of the first product(√{square root over ((Σx²)(Σy²))}{square root over ((Σx²)(Σy²))}) (block712) as shown in Equation 1 (block 710). The example process of FIG. 7then ends.

Turning now to FIGS. 8A and 8B, the depicted example flow diagram isrepresentative of an example process that may be performed by thecorrelator 214 of FIG. 2 to determine correlation coefficients (r) basedon Equation 2 above. In the illustrated example, the example process ofFIGS. 8A and 8B may be used to implement the operation of block 612 ofFIG. 6A.

Initially, the correlator 214 determines a sum of squared referenceaudio samples (Σx²) (block 802) based on the square of each audio samplein the reference audio samples 202. The correlator 214 then multipliesthe sum of squared reference audio samples (Σx²) by a total samplequantity value (n) (block 804) representing the total quantity of audiosamples in the reference and non-stationary audio samples 202 and 204being processed to determine a first product (n(Σx²)) and determines afirst square root of the first product (√{square root over (n(Σx²))})(block 806). The correlator 214 also determines a sum of squarednon-stationary audio samples (Σy²) (block 808) based on the square ofeach audio sample in the non-stationary audio samples 204 and multipliesthe sum of squared non-stationary audio samples (Σy²) by the totalsample quantity value (n) to determine a second product (n(Σy²))(block810). The correlator 214 determines a square of a sum of thenon-stationary audio samples 204 ((Σy)²) (block 812) and determines asecond square root of the difference between the second product (n(Σy²))and the square of the sum of the non-stationary audio samples 204((Σy)²) (i.e., √{square root over (n(Σy²)−(Σy)²)}{square root over(n(Σy²)−(Σy)²)}) (block 814). The correlator 214 multiplies the firstsquare root result (√{square root over (n(Σx²))}) by the second squareroot result (√{square root over (n(Σy²)−(Σy)²)}{square root over(n(Σy²)−(Σy)²)}) to determine a third product (√{square root over(n(Σx²))}√{square root over (n(Σy²)−(Σy)²)}{square root over(n(Σy²)−(Σy)²)}) (block 816) (FIG. 8B). The correlator 214 determines asum of cross-sample products (Σxy) (block 818) based on products (xy) ofeach reference audio sample 202 (x) and a corresponding one of thenon-stationary audio samples 204 (y). The correlator 214 multiplies thesum of cross-sample products (Σxy) by the total sample quantity value(n) to determine a fifth product (nΣxy) (block 820). The correlator 214then determines the correlation coefficient (r) by dividing the fifthproduct (nΣxy) by the third product (√{square root over(n(Σx²))}√{square root over (n(Σy²)−(Σy)²)}{square root over(n(Σy²)−(Σy)²)}) as shown in Equation 2 above (block 822). The exampleprocess of FIGS. 8A and 8B then ends.

Turning now to FIG. 9, the depicted example flow diagram isrepresentative of an example process that may be performed by thecorrelator 314 of FIG. 3 to determine correlation coefficients (r) basedon Equation 4 above. In the illustrated example, the example process ofFIG. 9 may be used to implement the operation of block 626 of FIG. 6B.

Initially, the correlator 314 determines a sum of reference complexconjugate pairs ((ΣXX*)) of the reference frequency-domainrepresentation 502 (block 902). The correlator 314 determines a sum ofnon-stationary complex conjugate pairs ((ΣYY*)) of the non-stationaryfrequency-domain representation 504 (block 904). The correlator 314 thenmultiplies the sum of reference complex conjugate pairs ((ΣXX*)) by thesum of non-stationary complex conjugate pairs ((ΣYY*)) to determine afirst product ((ΣXX*)(ΣYY*)) (block 906). The correlator 314 determinesthe square root of the first product (√{square root over((ΣXX*)(ΣYY*))}{square root over ((ΣXX*)(ΣYY*))}) (block 908). Thecorrelator 314 multiplies reference complex numbers of the referencefrequency-domain representation 502 by corresponding non-stationaryconjugates of the non-stationary frequency-domain representation 504 todetermine second products (XY*) (block 910) and determines a third sumof the second products (ΣXY*) (block 912). The correlator 314 determinesa correlation coefficient (r) associated with the reference andnon-stationary audio samples 202 and 204 by dividing the third sum ofthe second products (ΣXY*) by the square root of the first product(√{square root over ((ΣXX*)(ΣYY*))}{square root over ((ΣXX*)(ΣYY*))}) asshown in Equation 4 above (block 914). The example process of FIG. 9then ends.

Turning now to FIGS. 10A-10C, the depicted example flow diagram isrepresentative of an example process that may be performed by thecorrelator 314 of FIG. 3 to determine correlation coefficients (r) basedon Equation 5 above. In the illustrated example, the example process ofFIGS. 10A-10C may be used to implement the operation of block 626 ofFIG. 6B.

Initially, the correlator 314 determines a sum of reference complexconjugate pairs ((ΣXX*)) of the reference frequency-domainrepresentation 502 (block 1002). The correlator 314 multiplies the sumof reference complex conjugate pairs ((ΣXX*)) by a total sample quantityvalue (n) representing the total quantity of the audio samples of thereference and non-stationary audio samples 202 and 204 to determine afirst product (n(ΣXX*)) (block 1004). The correlator 314 determines asquare root of the first product (√{square root over (n(ΣXX*))}) (block1006). The correlator 314 determines a sum of non-stationary complexconjugate pairs ((ΣYY*)) of the non-stationary frequency-domainrepresentation 504 (block 1008) and multiplies the sum of non-stationarycomplex conjugate pairs ((ΣYY*)) by the total sample quantity value (n)to determine a second product (n(ΣYY*)) (block 1010). The correlator 314determines a sum of non-stationary complex numbers ((ΣY)) of thenon-stationary frequency-domain representation 504 (block 1012) anddetermines a conjugate of the sum of non-stationary complex numbers((ΣY)) (i.e., (ΣY)*) (block 1014) (FIG. 10B). The correlator 314multiplies the sum of non-stationary complex numbers ((ΣY)) by itsconjugate ((ΣY)*) to generate a third product ((ΣY)(ΣY)*) (block 1016).The correlator 314 determines a second square root of the differencebetween the second product (n(ΣYY*)) and the third product ((ΣY)(ΣY)*)(i.e., √{square root over (n(ΣYY*)−(ΣY)(ΣY)*)}{square root over(n(ΣYY*)−(ΣY)(ΣY)*)}{square root over (n(ΣYY*)−(ΣY)(ΣY)*)}) (block1018). The correlator 314 multiplies the first square root (√{squareroot over (n(ΣXX*))}) by the second square root (√{square root over(n(ΣYY*)−(ΣY)(ΣY)*)}{square root over (n(ΣYY*)−(ΣY)(ΣY)*)}{square rootover (n(ΣYY*)−(ΣY)(ΣY)*)}) to determine a third product (√{square rootover (n(ΣXX*))}√{square root over (n(ΣYY*)−(ΣY)(ΣY)*)}{square root over(n(ΣYY*)−(ΣY)(ΣY)*)}{square root over (n(ΣYY*)−(ΣY)(ΣY)*)}) (block1020). The correlator 314 multiplies reference complex numbers (X) ofthe reference frequency-domain representation 502 by correspondingnon-stationary conjugates (Y*) of the non-stationary frequency-domainrepresentation 504 to determine fourth products (XY*) (block 1022) anddetermines a sum of the fourth products (ΣXY*) (block 1024). Thecorrelator 314 multiplies the sum of the fourth products (ΣXY*) by thetotal sample quantity value (n) to determine a fifth product (nΣXY*)(block 1026) (FIG. 10C). The correlator 314 determines a correlationcoefficient (r) by dividing the fifth product (nΣXY*) by the thirdproduct (√{square root over (n(ΣXX*))}√{square root over(n(ΣYY*)−(ΣY)(ΣY)*)}{square root over (n(ΣYY*)−(ΣY)(ΣY)*)}{square rootover (n(ΣYY*)−(ΣY)(ΣY)*)}) as shown in Equation 5 (block 1028). Theexample process of FIGS. 10A-10C then ends.

Turning now to FIGS. 11A-11C, the depicted example flow diagram isrepresentative of an example process that may be performed by thecorrelator 314 of FIG. 3 to determine correlation coefficients (r) basedon Equation 6 above. In the illustrated example, the example process ofFIGS. 11A-11C may be used to implement the operation of block 626 ofFIG. 6B.

Initially, the correlator 314 determines a sum of reference complexconjugate pairs ((ΣXX*)) of the reference frequency-domainrepresentation 502 (block 1102). The correlator 314 multiplies the sumof reference complex conjugate pairs ((ΣXX*)) by a total sample quantityvalue (n) representing the total quantity of the audio samples of thereference and non-stationary audio samples 202 and 204 to determine afirst product (n(ΣXX*)) (block 1104). The correlator 314 determines asum of reference complex numbers ((ΣX)) of the referencefrequency-domain representation 504 (block 1106) and determines aconjugate of the sum of reference complex numbers ((ΣX)) (i.e., (ΣX)*)(block 1108). The correlator 314 multiplies the sum of reference complexnumbers ((ΣX)) by its conjugate ((ΣX)*) to generate a second product((ΣX)(ΣX)*) (block 1110). The correlator 314 determines a first squareroot of the difference between the first product (n(ΣXX*)) and thesecond product ((ΣX)(ΣX)*) (i.e., √{square root over(n(ΣXX*)−(ΣX)(ΣX)*)}{square root over (n(ΣXX*)−(ΣX)(ΣX)*)}{square rootover (n(ΣXX*)−(ΣX)(ΣX)*)}) (block 1112).

The correlator 314 determines a sum of non-stationary complex conjugatepairs ((ΣYY*)) of the non-stationary frequency-domain representation 504(block 1114) (FIG. 11B) and multiplies the sum of non-stationary complexconjugate pairs ((ΣYY*)) by the total sample quantity value (n) todetermine a third product (n(ΣYY*)) (block 1116). The correlator 314determines a sum of non-stationary complex numbers ((ΣY)) of thenon-stationary frequency-domain representation 504 (block 1118) anddetermines a conjugate of the sum of non-stationary complex numbers((ΣY)) (i.e., (ΣY)*) (block 1120). The correlator 314 multiplies the sumof non-stationary complex numbers ((ΣY)) by its conjugate ((ΣY)*) togenerate a fourth product ((ΣY)(ΣY)*) (block 1122). The correlator 314determines a second square root of the difference between the thirdproduct (n(ΣYY*)) and the fourth product ((ΣY)(ΣY)*) (i.e., √{squareroot over (n(ΣYY*)−(ΣY)(ΣY)*)}{square root over(n(ΣYY*)−(ΣY)(ΣY)*)}{square root over (n(ΣYY*)−(ΣY)(ΣY)*)}) (block1124).

The correlator 314 multiples the first and second square roots todetermine a fifth product (√{square root over(n(ΣXX*)−(ΣX)(ΣX)*)}{square root over (n(ΣXX*)−(ΣX)(ΣX)*)}{square rootover (n(ΣXX*)−(ΣX)(ΣX)*)}√{square root over (n(ΣYY*)−(ΣY)(ΣY)*)}{squareroot over (n(ΣYY*)−(ΣY)(ΣY)*)}{square root over (n(ΣYY*)−(ΣY)(ΣY)*)})(block 1126) (FIG. 11C). The correlator 314 multiplies reference complexnumbers (X) of the reference frequency-domain representation 502 bycorresponding non-stationary conjugates (Y*) of the non-stationaryfrequency-domain representation 504 to determine sixth products (XY*)(block 1128) and determines a sum of the sixth products (Σ(XY*)) (block1130). The correlator 314 multiplies the sum of the sixth products(Σ(XY*)) by the total sample quantity value (n) to determine a seventhproduct (nΣ(XY*)) (block 1132).

The correlator 314 determines a sum of non-stationary conjugates ((ΣY*))(block 1134). The correlator 314 multiples the sum of non-stationaryconjugates by the sum of reference complex numbers to determine aneighth product ((ΣX)(ΣY*)) (block 1136). The correlator 314 determines acorrelation coefficient (r) by dividing the difference between theeighth product ((ΣX)(ΣY*)) and seventh product (nΣ(XY*)) by the fifthproduct (√{square root over (n(ΣXX*)−(ΣX)(ΣX)*)}{square root over(n(ΣXX*)−(ΣX)(ΣX)*)}{square root over (n(ΣXX*)−(ΣX)(ΣX)*)}√{square rootover (n(ΣYY*)−(ΣY)(ΣY)*)}{square root over (n(ΣYY*)−(ΣY)(ΣY)*)}{squareroot over (n(ΣYY*)−(ΣY)(ΣY)*)}) as shown in Equation 6 (block 1138). Theexample process of FIGS. 11A-11C then ends.

FIG. 12 is a block diagram of an example processor system 1210 that maybe used to implement the example apparatus, methods, and systemsdescribed herein. As shown in FIG. 12, the processor system 1210includes a processor 1212 that is coupled to an interconnection bus1214. The processor 1212 may be any suitable processor, processing unit,or microprocessor. Although not shown in FIG. 12, the system 1210 may bea multi-processor system and, thus, may include one or more additionalprocessors that are identical or similar to the processor 1212 and thatare communicatively coupled to the interconnection bus 1214.

The processor 1212 of FIG. 12 is coupled to a chipset 1218, whichincludes a memory controller 1220 and an input/output (I/O) controller1222. A chipset provides I/O and memory management functions as well asa plurality of general purpose and/or special purpose registers, timers,etc. that are accessible or used by one or more processors coupled tothe chipset 1218. The memory controller 1220 performs functions thatenable the processor 1212 (or processors if there are multipleprocessors) to access a system memory 1224 and a mass storage memory1225.

In general, the system memory 1224 may include any desired type ofvolatile and/or non-volatile memory such as, for example, static randomaccess memory (SRAM), dynamic random access memory (DRAM), flash memory,read-only memory (ROM), etc. The mass storage memory 1225 may includeany desired type of mass storage device including hard disk drives,optical drives, tape storage devices, etc.

The I/O controller 1222 performs functions that enable the processor1212 to communicate with peripheral input/output (I/O) devices 1226 and1228 and a network interface 1230 via an I/O bus 1232. The I/O devices1226 and 1228 may be any desired type of I/O device such as, forexample, a keyboard, a video display or monitor, a mouse, etc. Thenetwork interface 1230 may be, for example, an Ethernet device, anasynchronous transfer mode (ATM) device, an 802.11 device, a digitalsubscriber line (DSL) modem, a cable modem, a cellular modem, etc. thatenables the processor system 1210 to communicate with another processorsystem.

While the memory controller 1220 and the I/O controller 1222 aredepicted in FIG. 12 as separate functional blocks within the chipset1218, the functions performed by these blocks may be integrated within asingle semiconductor circuit or may be implemented using two or moreseparate integrated circuits.

Although the above discloses example methods, apparatus, systems, andarticles of manufacture including, among other components, firmwareand/or software executed on hardware, it should be noted that suchmethods, apparatus, systems, and articles of manufacture are merelyillustrative and should not be considered as limiting. Accordingly,while the above describes example methods, apparatus, systems, andarticles of manufacture, the examples provided are not the only ways toimplement such methods, apparatus, systems, and articles of manufacture.

Although certain example methods, apparatus, systems, and articles ofmanufacture have been described herein, the scope of coverage of thispatent is not limited thereto. On the contrary, this patent covers allmethods, apparatus and articles of manufacture fairly falling within thescope of the claims of this patent.

What is claimed is:
 1. A method to credit media presented by a mediapresentation device, the method comprising: determining, via a neuralnetwork, a first distance of a first portable audio detector from astationary unit, the first portable audio detector associated with afirst panelist, the stationary unit located in proximity to the mediapresentation device; determining, via the neural network, a seconddistance of a second portable audio detector from the stationary unit,the second portable audio detector associated with a second panelist; inresponse to the first distance being less than a threshold distance,crediting the media with an exposure credit, the exposure creditindicative of the media being exposed to the first panelist; and inresponse to the second distance being more than the threshold distance,not crediting the media as exposed to the second panelist.
 2. A methodas defined in claim 1, wherein the neural network is trainable to detectdifferent distances of portable audio detectors by analyzing audiosamples corresponding to different training distances in a monitoringenvironment.
 3. A method as defined in claim 1, wherein the stationaryunit is separate from the media presentation device.
 4. A method asdefined in claim 1, wherein the determining of the first distance occursin real-time when first audio samples are collected by the stationaryunit from the first portable audio detector.
 5. A method as defined inclaim 1, wherein the determining of the first distance includes using apost-process after first audio samples are received.
 6. A method tocredit media presented by a media presentation device, the methodcomprising: determining, using a neural network, a first distance of afirst portable audio detector from a stationary unit, the first portableaudio detector associated with a first panelist, the stationary unitlocated in proximity to the media presentation device, wherein theneural network determines the first distance by performing a correlationanalysis on first audio samples of the media collected by the stationaryunit and on second audio samples collected by the first portable audiodetector; determining, using the neural network, a second distance of asecond portable audio detector from the stationary unit, the secondportable audio detector associated with a second panelist; in responseto the first distance being less than a threshold distance, creditingthe media as exposed to the first panelist; and in response to thesecond distance being more than the threshold distance, not creditingthe media as exposed to the second panelist.
 7. A method as defined inclaim 6, wherein the correlation analysis is performed in the frequencydomain.
 8. A tangible machine accessible medium comprising instructionsthat, when executed, cause a machine in communication with a stationaryunit located adjacent to a media presentation device that is to presentmedia to at least: determine, via a neural network, a first distance ofa first portable audio detector from the stationary unit, the firstportable audio detector exposed to the media and associated with a firstpanelist; determine, via the neural network, a second distance of asecond portable audio detector from the stationary unit, the secondportable audio detector exposed to the media and associated with asecond panelist; in response to the first distance being less than athreshold distance, credit the media with an exposure credit, theexposure credit indicative of the media being exposed to the firstpanelist; and in response to the second distance being more than thethreshold distance, not credit the media as exposed to the secondpanelist.
 9. The tangible machine accessible medium of claim 8, whereinthe instructions further cause the machine to train the neural networkto detect different distances of portable audio detectors by analyzingaudio samples corresponding to different training distances in amonitoring environment.
 10. The tangible machine accessible medium ofclaim 8, wherein the stationary unit is separate from the mediapresentation device.
 11. The tangible machine accessible medium of claim8, wherein the instructions are further to cause the machine todetermine, via the neural network, the first distance of the firstportable audio detector from the stationary unit in real-time when firstaudio samples are collected from the first portable audio detector. 12.The tangible machine accessible medium of claim 8, wherein theinstructions are further to cause the machine to determine, via theneural network, the first distance of the first portable audio detectorfrom the stationary unit at a time after first audio samples arecollected by the stationary unit from the first portable audio detector.13. A tangible machine accessible medium comprising instructions that,when executed, cause a machine in communication with a stationary unitlocated adjacent a media presentation device that is to present media toat least: determine, via a neural network, a first distance of a firstportable audio detector from the stationary unit by performing acorrelation analysis on first audio samples of the media collected bythe stationary unit and on second audio samples collected by the firstportable audio detector, the first portable audio detector exposed tothe media and associated with a first panelist; determine, via theneural network, a second distance of a second portable audio detectorfrom the stationary unit, the second portable audio detector exposed tothe media and associated with a second panelist; in response to thefirst distance being less than a threshold distance, credit the media asexposed to the first panelist; and in response to the second distancebeing more than the threshold distance, not credit the media as exposedto the second panelist.
 14. The tangible machine accessible medium ofclaim 13, wherein the neural network is to perform the correlationanalysis in the frequency domain.
 15. A processing unit in communicationwith a stationary unit located in proximity to a media presentationdevice that is to present media, the processing unit comprising: aneural network to: determine a first distance of a first portable audiodetector from the stationary unit, the first portable audio detectorassociated with a first panelist; and determine a second distance of asecond portable audio detector from the stationary unit, the secondportable audio detector associated with a second panelist; and aprocessor to: in response to the first distance being less than athreshold distance, store an exposure credit to indicate the media wasexposed to the second panelist; and in response to the second distancebeing more than the threshold distance, not store the exposure credit toindicate the media was exposed to the second panelist.
 16. A processingunit as defined in claim 15, wherein the neural network is trainable todetect different distances of portable audio detectors by analyzingaudio samples corresponding to different training distances in amonitoring environment.
 17. A processing unit as defined in claim 15,wherein the stationary unit is separate from the media presentationdevice.
 18. A processing unit as defined in claim 15, wherein the neuralnetwork is to determine the first distance of the first portable audiodetector from the stationary unit in real-time when first audio samplesare collected by the stationary unit from the first portable audiodetector.
 19. A processing unit as defined in claim 15, wherein theneural network is to determine the first distance of the first portableaudio detector from the stationary unit by executing a post-processafter first audio samples are received.
 20. An apparatus comprising: aprocessor; and a memory to store machine readable instructions that,when executed by the processor, cause the processor to performoperations including: determining, using a neural network, a firstdistance of a first portable audio detector from a stationary unit, thefirst portable audio detector associated with a first panelist, thestationary unit located in proximity to a media presentation device thatis to present media, wherein the neural network determines the firstdistance by performing a correlation analysis on first audio samples ofthe media collected by the stationary unit and on second audio samplescollected by the first portable audio detector; determining, using theneural network, a second distance of a second portable audio detectorfrom the stationary unit, the second portable audio detector associatedwith a second panelist; in response to the first distance being lessthan a threshold distance, crediting the media as exposed to the firstpanelist; and in response to the second distance being more than thethreshold distance, not crediting the media as exposed to the secondpanelist.
 21. An apparatus as defined in claim 20, wherein the processoris to perform the correlation analysis in the frequency domain.